Optimizing hydrocarbon recovery through integrated utilization of geomechanics and injection/production using machine learning

ABSTRACT

Systems and methods include a computer-implemented method for optimized injection/production and placement of wells. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.

TECHNICAL FIELD

The present disclosure applies to optimizing hydrocarbon recovery.

BACKGROUND

Well placement decisions made for wells to be fractured require the knowledge of geomechanics, how the stress changes and geomechanics interact with injection and production both spatially and temporally, and the location, direction, and spacing of the wells. Lack of a complete understanding of this knowledge can lead to poor decisions in well placement. In conventional systems and in current applications, geomechanics is taken into account locally when drilling a new well, without the well-and field-level knowledge of future temporal and spatial predictions of relationships between geomechanics and well depletion.

SUMMARY

The present disclosure describes techniques that can be used for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs. In some implementations, a computer-implemented method includes the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.

The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method, the instructions stored on the non-transitory, computer-readable medium.

The subject matter described in this specification can be implemented in particular implementations, so as to realize one or more of the following advantages. Techniques of the present disclosure can enable teams to make better decisions in well placement, hydraulic fracturing, (re)fracturing, reservoir management, and depletion decisions. This can lead to better field development decisions through improved knowledge and understanding of relationships between injection/production of fluids from a reservoir and the geomechanical behavior/stress changes that control the fracture growth/orientation, and thus well placement. The techniques can use the data/tools (e.g., reservoir simulation and machine learning) from proven processes in the industry and based on reservoir simulation studies that are conducted using geomechanics with industry/benchmark simulators.

The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the accompanying drawings, and the claims. Other features, aspects, and advantages of the subject matter will become apparent from the Detailed Description, the claims, and the accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram of an example of a workflow for a single model in two dimensions (2D), according to some implementations of the present disclosure.

FIG. 2 is a graph showing an example of a single-well model, according to some implementations of the present disclosure.

FIG. 3 is a three-dimensional (3D) plot showing a location of a single well in the model, according to some implementations of the present disclosure.

FIG. 4 is a plot showing an example of a single well pressure distribution for a single well model, according to some implementations of the present disclosure.

FIG. 5 is a graph showing an example of tubing performance plots, according to some implementations of the present disclosure.

FIG. 6 is a diagram showing an example of a first part of a case study, according to some implementations of the present disclosure.

FIG. 7 is a graph showing examples of cumulative oil plots for three base cases, according to some implementations of the present disclosure.

FIG. 8 is a graph showing examples of cumulative gas plots for three base cases, according to some implementations of the present disclosure.

FIG. 9 is a diagram showing examples of grid names for a shear strain and shear stress investigation, according to some implementations of the present disclosure.

FIG. 10 is a diagram showing one of the grids investigated for shear strain and shear stress, according to some implementations of the present disclosure.

FIG. 11 is a graph showing example experimental design results by CMOST, according to some implementations of the present disclosure.

FIG. 12 is a graph showing example shear strains in block 106,106,3, according to some implementations of the present disclosure.

FIG. 13 is a three-dimensional plot showing examples of different phenomena of shear strain between the toe and the heel of the well within the IJ direction, according to some implementations of the present disclosure.

FIG. 14 is a graph showing example the shear stress in block 106,106,3 for three axes, according to some implementations of the present disclosure.

FIG. 15 is a three-dimensional virtual plot showing examples of a dominance of the shear stress in the IJ direction within layers of grid 2 and grid 3, according to some implementations of the present disclosure.

FIG. 16 is a diagram showing an example of a second part of a case study (parent and child), according to some implementations of the present disclosure.

FIG. 17 is a plot showing an example of a gas saturation distribution of a parent well and a child well, according to some implementations of the present disclosure.

FIGS. 18A and 18B are graphs showing examples of cumulative gas plots for the parent well and the child well, according to some implementations of the present disclosure.

FIG. 19 is a plot showing an example of a gas saturation distribution of a parent well and a child well, according to some implementations of the present disclosure.

FIG. 20 is a 3D view showing examples of shear strain in a parent well and a child well, according to some implementations of the present disclosure.

FIG. 21 is a graph showing example shear strains in block 106,106,3 from the parent and child model, according to some implementations of the present disclosure.

FIG. 22 is a 3D view showing examples of shear stress in a parent well and a child well, according to some implementations of the present disclosure.

FIG. 23 is a graph showing example shear stress in block 106,106,3 from the parent and child model, according to some implementations of the present disclosure.

FIG. 24 is a graph showing example well head pressures of the parent well and the child well, according to some implementations of the present disclosure.

FIGS. 25-30 show example results of using models and predictions, according to some implementations of the present disclosure.

FIG. 31 is a flowchart of an example of a method for determining optimum stress distributions for the placement of new wells, according to some implementations of the present disclosure.

FIG. 32 is a block diagram illustrating an example computer system used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure, according to some implementations of the present disclosure.

Like reference numbers and designations in the various drawings indicate like elements.

DETAILED DESCRIPTION

The following detailed description describes techniques for optimizing hydrocarbon recovery using hydraulic fractures in hydrocarbon reservoirs. In some implementations, the well placement, fracturing, and fracture design can be optimized based on optimum injection and production of reservoir fluids to/from the reservoir to exploit stress distribution for better placement of well and fractures. Optimum results or optimization can be defined or measured, for example, as achieving results that provide increases in production above a pre-determined threshold (e.g., volume or percentage). In some implementations, techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs can be implemented as software applications that run on a processor of a computing device. Various modifications, alterations, and permutations of the disclosed implementations can be made and will be readily apparent to those of ordinary skill in the art, and the general principles defined may be applied to other implementations and applications, without departing from scope of the disclosure. In some instances, details unnecessary to obtain an understanding of the described subject matter may be omitted so as to not obscure one or more described implementations with unnecessary detail and inasmuch as such details are within the skill of one of ordinary skill in the art. The present disclosure is not intended to be limited to the described or illustrated implementations, but to be accorded the widest scope consistent with the described principles and features.

In some implementations, techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs can include steps for determining the relationship between the stress changes and the injection and production of fluids to/from a reservoir through geomechanical reservoir simulation. The techniques can include modeling and predicting the stress distribution in the reservoir in time and space with new wells drilled and associated reservoir rock depleted, and reservoir characteristics to determine fracture design and orientation needed for optimum well placement for maximum recovery of hydrocarbons. Stress distribution in the reservoir can be exploited through the injection/production of fluids with the understanding of stress distribution vertically/areally and in time.

As previously described, although usually effective, hydraulic fractures need to be designed under the light of reservoir characteristics. Moreover, fracking jobs should be performed carefully because, despite some advantages, controlling the growth and size and maintaining the desired orientation are typically difficult due to rock’s geomechanical behavior being susceptible to changes in stress distributions. The stress distributions include locations in which undesired and/or uncontrollable changes in orientation of fractures may happen along with compaction and dilation. This can result in poor reservoir management and well failures due to stress changes originating from injection and production of fluids into/from the reservoir rock. Due to low matrix permeability, the majority of the hydrocarbon recovery comes from the parts of the reservoir that fractures extend and serve as a conduit for flow. In this sense, the control of fracture size and fracture orientation on the effects of orientation are significant in optimizing recovery.

Implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs described in this specification solve such problems by optimizing the mechanisms of recovery associated with differing fracture orientations along with the physics. This causes fracture re-orientation due to stress changes in the rock originating from injection and withdrawal of fluids. The techniques use not only the spatial components but also temporal components involved in the problem. The techniques are useful in optimizing fracture orientations in fracturing of replacement/development wells and/or re-fracturing of existing wells leading to higher recovery of hydrocarbons through more optimum fracture orientations.

Implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs described in the present disclosure differ from and improve upon currently existing techniques. In particular, some implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs differ by using reservoir stress distribution and inter-well connectivity information to optimize fractures.

In addition, some implementations of the techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs improve upon the currently existing options by using stress measurements so that fracturing and fracture sizes can be optimized by using optimum injection/production of reservoir fluids into/from the reservoir to adjust stress distribution for better placement of fractures.

The techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs of the present disclosure can include various workflows. The lists of possible constituent steps of workflows and methods is intended to be exemplary only and not intended to limiting when optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs. Persons having ordinary skill in the art relevant to the present disclosure will understand that equivalent steps can be substituted without changing the essential function or operation of techniques for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs.

In some implementations, a workflow can include the following. Relationship data is obtained that defines relationships between stress changes in space and time and injection/production of fluids to/from reservoir. Stress distribution data for the reservoir is obtained using reservoir geomechanical modeling tools. Fracture growth/propagation behavior under existing stress distributions data is obtained using fracture modeling software and geomechanical properties to optimize treatment. Relationships are analyzed between: fluid injection/withdrawal and geomechanical changes and resulting stress distributions, the stress distribution in the reservoir, and reservoir geomechanical and flow characteristics. The analysis can be used to determine fracture design and orientation needed for optimum recovery of hydrocarbons. The techniques can use machine learning to recognize patterns and relationships between the injection/production and stress changes/distributions using the geomechanical reservoir simulation inputs and outputs in time, and then to optimize well placement and fracture design without the need for reservoir modelling. Stress distribution in the reservoir can be exploited using injection/production of fluids and by optimizing injection/production and placement of wells, with accordingly-designed fractures. The resulting data can be analyzed, e.g., using machine learning, to adjust injection and production of fluids to/from the reservoir. As a result, an optimum stress distribution can be obtained for placing new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons.

Various elements of a workflow (e.g., FIG. 1 ) for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs of the present disclosure can be related in the following way, as an example. The steps of the workflow are not intended to limit the scope or nature of the relationships between the various elements, and the following examples are presented as illustrative examples only. The relationships and the stress distribution in the reservoir are required to determine the time and location of well placement, fracture design, and orientation needed for optimum recovery of hydrocarbons. Then, application can be made of injection/production of fluids with the previous information and stress distribution, both vertically and spatially. The process can be repeated for all patterns or blocks in the reservoir.

The workflow of FIG. 1 for optimizing hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs generally can include the following. Relationship information is obtained that is associated with relationships between fluids withdrawal/depletion and geomechanics/stress distribution. The relationship information is based on data collected from injection/production of wells and the stress distribution in the reservoir. The relationship information can be used to provide information about azimuths and maximum/minimum stress directions, existing fracture orientations, and fracture size and orientation needed for optimum recovery of hydrocarbons, based on an analysis of the relationship. The relationship information can define stress distributions in the reservoir, and can be used to modify stress distributions in the reservoir, e.g., based on an application of injection/production of fluids with the previous information on stress distribution, both vertically and areally.

Conceptual models can be developed to determine or predict the effect of stress and strain in each fracture in a horizontal well. Initially, a first model, for example, can be established for a single well, and analysis can be performed for a single well. A second model can be established for a pair of wells, including a parent well and a child well, and the analysis can be compared to the analysis of the single well and the first model.

Models can be built, for example, using a Gaussian Emulation Machine (GEM) Simulator and Sensitivity approach designed for 100 datasets each. Both models can use a public data set associated with the Eagle Ford shale reservoir located in South Texas, US. The model type is Cartesian (211 *211 *5). Table 1 shows example values of initial conditions of components of gas condensate. The properties of each component for oil and condensate gas are shown in Tables 2 and 3.

TABLE 1 Initial Condition of Gas Cap Condensate Component ZGAS N2 0.03 C1 0.65 C2 0.104 C3 0.103 C4-6 0.07 C7+ 0.07 Total 1.00

FIG. 1 is a flow diagram of an example of a workflow 100 for a single model in two dimensions (2D), according to some implementations of the present disclosure. The workflow 100 can be used to optimize hydrocarbon recovery through utilization of hydraulic fractures in hydrocarbon reservoirs. The workflow 100 can also be used to manage and optimize field development, depletion, and well placement plans. This can provide not only well-level but a field-wide prior knowledge of geomechanics/fracture behavior and well depletion/placement coupled with machine learning (ML). The ML can be initially trained and fed with data from geomechanical reservoir simulation models and fracture propagation models. The ML can be also be provided with field data as it becomes available to update, in order to run scenarios to optimize future well placement and for depletion planning. As ML captures the complex relationship between the important inputs and outputs, an ML model can be initially fed and trained by simulation data and field data as it becomes available.

In an example, a base case of a single well model can have a fracture width 50 feet (ft), an intrinsic effective permeability of 50 millidarcies (mD), and a tip permeability of 5 mD. Other parameters in this example include a half-length of fracture of 500 ft, a grid cell width of 50 ft, a bottom hole pressure (BHP) of 4,500 pounds per square inch (psi), and a well head pressure (WHP) of 1,300 psi.

At 102, reservoir and geomechanical data are collected to build a geomechanical reservoir simulation model. At 104, relationship data is obtained between stress changes and distributions in time and space and with respect to injection and production of fluids to and from the reservoir. Stress distribution in the reservoir can be obtained by running geomechanical reservoir simulation models. At 106, fracture growth/propagation behavior under existing stress distributions is generated using fracture modeling software and geomechanical properties to optimize well treatment. At 108, an analysis is performed on the relationship, the stress distribution in the reservoir, and reservoir characteristics to determine optimum time and location for well placement, fracture design and orientation needed for maximum recovery of hydrocarbons. At 110, well injection and production data are collected for each reservoir layer that is completed. At 112, the injection and production data is evaluated to adjust the injection and production to/from the reservoir to obtain an optimum stress distribution to place new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons using ML. For example, stress distribution in the reservoir can be exploited using injection and production of fluids with the previous information on the relationship and stress distribution, vertically and areally. ML is used to recognize the pattern and relationship between the injection/production and stress changes/distributions using the geomechanical reservoir simulation inputs and outputs in time. At 114, steps 102-112 are continuously repeated to optimize the well placement and subsequent fracture design without the need for reservoir modelling.

Table 2 shows example values of compositional data of an example hydrocarbon-producing geological formation extending over a large region (e.g., Eagle Ford Oil). The values include a molecular weight (MW), a critical temperature (Tc), a critical pressure (Pc) in pounds per square inch absolute (psia), and a critical volume (Vc) measured in cubic feet per pound mass (cft/lbm) for each composition.

TABLE 2 Example Compositional Data Composition MW Specific Gravity Acentric Factor Tc R Pc, Psia Vc, cft/lbm C1 16.064 0.35 0.013 343.3 673.1 1.5658 N2 28.01 0.808 0.04 227.2 492.3 1.4256 C2 30.07 0.48 0.0986 549.8 708.4 2.3556 C3 44.1 0.5077 0.1524 665.8 617.4 3.2294 CO2 44.01 0.8159 0.225 547.6 1071.3 1.5126 IC4 58.12 0.5631 0.1848 734.6 529.1 4.2127 NC4 58.12 0.5844 0.201 765.4 550.7 4.1072 IC5 72.15 0.6248 0.2223 828.7 483.5 4.9015 NC5 72.15 0.6312 0.2539 845.6 489.5 5.0232 NC6 86.18 0.6641 0.3007 914.2 439.7 5.9782 C7+ 114.4 0.7563 0.3739 1060.5 402.8 7.4093 C11+ 166.6 0.8135 0.526 1223.6 307.7 10.682 C15+ 230.1 0.8526 0.6979 1368.4 241.4 14.739 C20+ 409.2 0.9022 1.0456 1614.2 151.1 26.745

Table 3 shows another set of example values of compositional data of an example hydrocarbon-producing geological formation extending over a large region (e.g., Eagle Ford Condensate).

TABLE 3 Compositional Data of Eagle Ford Condensate (Kamari, et al., 2018) Composition MW Specific Gravity Acentric Factor Tc R Pc, Psia Vc, cft/lbm C1 16.04 0.35 0.013 343.26 673.08 1.5658 N2 28.01 0.808 0.04 227.16 492.32 1.4256 C2 30.07 0.48 0.0986 549.774 708.35 2.3556 C3 44.1 0.5077 0.1524 665.82 617.38 3.2294 CO2 44.01 0.8159 0.225 547.56 1071.3 1.5126 IC4 58.12 0.5631 0.1848 734.58 529.06 4.2127 NC4 58.12 0.5844 0.201 765.36 550.66 4.1072 IC5 72.15 0.6248 0.2223 828.72 483.5 4.9015 NC5 72.15 0.6312 0.2539 845.64 489.52 5.0232 NC6 86.18 0.6641 0.3007 914.22 439.7 5.9782 C7+ 112 0.7527 0.3673 1051.39 408.59 7.261 C11+ 175 0.8201 0.5491 1245.9 296.89 11.2083 C15+ 210 0.8424 0.6435 1327.59 259.01 13.435 C20+ 250 0.8612 0.7527 1405.81 226.28 16.0488

FIG. 2 is a graph 200 showing an example of a single-well model, according to some implementations of the present disclosure. The single well model contains a lateral section and a fracture in the vicinity of a wellbore (FIG. 1 ).

FIG. 3 is a three-dimensional plot 300 showing a location 308 of a single well in the model, according to some implementations of the present disclosure. The plot 300 is plotted relative to an i-direction 302, a j-direction 304, a k-direction 306, and intensity keys 310 and 312.

FIG. 4 is a plot 400 showing an example of a single well pressure distribution 410 for a single well model, according to some implementations of the present disclosure. The distribution is plotted relative to an i-direction 402, a j-direction 404, and k-direction 406, with intensities indicated by a pressure intensity key 408. The fracture permeability nearby the wellbore is 20 md, and the fracture tip is 5 md. During analysis of the techniques of the present disclosure, a horizontal well model was developed using a Prosper simulator to generate a tubing curve (FIG. 5 ). In this case, the well model uses a correlation of Kuchuk and Goode models.

FIG. 5 is a graph 500 showing an example of tubing performance plots 502, according to some implementations of the present disclosure. In this example, two modified models have been set up by multiplying the matrix permeability of a base-case for 10× and 100×. The plots 502 are plotted relative to a gas rate axis 504 (e.g., in cubic feet per day (ft3/day) and a bottom hole pressure (BHP) 506 (e.g., in psi). The results of three models in FIG. 6 and FIG. 7 show that there is significant difference in oil cumulative and gas cumulative performance. The difference of gas cumulative is up to 400,000 cubic feet, and oil cumulative is up to 4,000 bbl.

FIG. 6 is a diagram 600 showing an example of a first part of a case study, according to some implementations of the present disclosure. The study starts with a base case 602 (one well), with one permutation 604, 10 permutations 606, and 100 permutations 608.

FIG. 7 is a graph 700 showing examples of cumulative oil plots 702, 704, and 706 for three base cases, according to some implementations of the present disclosure. The plots 702, 704, and 706 are plotted relative to dates 708 and cumulative oil 710 (e.g., in bbl).

FIG. 8 is a graph 800 showing examples of cumulative gas plots 802, 804, and 806 for three base cases, according to some implementations of the present disclosure. The plots 802, 804, and 806 are plotted relative to dates 808 and cumulative gas 810 (e.g., in standard cubic feet (scft)).

A next stage is to evaluate BHP sensitivity in the three models base cases. As an example, a CMOST simulator can be used to run the sensitivity to save time in the running model. The sensitivity uses 100 different BHP datasets, starting from BHP 3,000 psi through 10,000 psi, with an increment of 70 psi. In this case, the experimental designs that is used include 100 experiments with identifiers (IDs) matching the 100 BHP dataset. The final results to be obtained are shear strain and shear stress in certain grid blocks for 6, 12, 60, and 120 months. The grid blocks investigated in the model are shown in FIG. 9 .

FIG. 9 is a diagram 900 showing examples of grid names 902-910 for a shear strain and shear stress investigation, according to some implementations of the present disclosure. The grid names 902-910 form aggregate 912.

FIG. 10 is a diagram 1000 showing one of the grids investigated for shear strain and shear stress, according to some implementations of the present disclosure. In this example, CMOST results show the optimum solution that can be obtained as a gas cumulative of 9.8×10⁸ ft³ (FIG. 11 ). From the optimum solution, the shear strain and shear stress can be investigated in the certain grid blocks. For example, regarding grid block 106,106,3, FIG. 12 exhibits the shear strain in block 106,106,3 in three directions. The shear strain in the IJ direction is a positive. The shear strain in the JK direction 1016 is a negative. The shear strain in the IK direction has an alternating direction from a negative to a positive direction, relative to points and axes 1002 to 1016.

FIG. 11 is a graph 1100 showing example experimental design results by CMOST, according to some implementations of the present disclosure. Results for a base case 1102, general solutions 1104, and an optimal solution 1106 are plotted relative to experiment identifier (ID) 1108 and cumulative gas 1110.

FIG. 12 is a graph 1200 showing example shear strains in block 106,106,3, according to some implementations of the present disclosure. The shear strains, in the IJ direction 1202, in the IK direction 1204, and in the JK direction 1206 are plotted relative to time 1208 and shear strain 1210.

FIG. 13 is a three-dimensional plot 1300 showing examples of different phenomena of shear strain 1310 between the toe and the heel of the well within the IJ direction, according to some implementations of the present disclosure. One side of the heel measures the positive number of shear strain, but the toe measures the negative one. Conversely, the phenomena occurs in an opposite way for other side. Shear strains are plotted relative to i-coordinates 1302, j-coordinates 1304, and k-coordinates 1306. Intensities of the shear strains are indicated by shading in an intensities key 1308.

FIG. 14 is a graph 1400 showing example the shear stress in block 106,106,3 for three axes, according to some implementations of the present disclosure. The shear stress in IJ direction is also positive. The shear stress in JK direction is a negative. The shear stress in the IK direction changes from negative to positive. The shear strains, in the IJ direction 1402, in the IK direction 1404, and in the JK direction 1406 are plotted relative to time 1408 and shear strain 1410.

FIG. 15 is a three-dimensional virtual plot 1500 showing examples of a dominance of the shear stress in the IJ direction within layers of grid 2 and grid 3, according to some implementations of the present disclosure. In this example, the shear stresses approach 6 psi, as indicated by shading in an intensities key 1508. Shear strains are plotted relative to an i-direction 1502, a j - direction 1504, and a k- direction 1506.

Another part of the case study is to evaluate the impact of a child well onto the parent well in terms of cumulative production as well as shear strain and shear strain (FIG. 16 ). Another horizontal well can be established beside the parent well. The new well can have a similar profile as its parent. For example, the distance between two wells can be 1500 ft, and the distance between tips of fracture can be 500 ft.

FIG. 16 is a diagram 1600 showing an example of a second part of a case study (parent and child), according to some implementations of the present disclosure. The study starts with a base case 602 (one well), with one permutation 604, 10 permutations 606, and 100 permutations 608. The position of the child well is right next to the parent well. The child well has the same completion as the parent well with 20 fractures. The horizontal length of the two wells is 5000 ft. The same technique has been conducted for the three models with respect to variant matrix permeability, 100 dataset pressure, and the grid blocks for shear strain and shear stress.

FIG. 17 is a plot 1700 showing an example of a gas saturation distribution of a parent well 1708 and a child well 1710, according to some implementations of the present disclosure. The gas saturation distribution is shown along both the wellbore and the vicinity of the wellbore. The pressure drawdown in the child well 1710 (“PROD2”) is greater than the parent well 1708. The impact of pressure drawdown is that gas production from the child well 1710 is greater than the parent well 1708. The distribution is plotted relative to an i-direction 1702, a j-direction 1704, and a k-direction 1706, with intensities indicated by a pressure intensity key 1712.

FIGS. 18A and 18B are graphs 1800, 1850 showing examples of cumulative gas plots for the parent well and the child well, according to some implementations of the present disclosure. Referring to FIG. 18A for the parent well, the graph 1800 includes plots 1802, 1804, and 1806 for three base cases. The plots 1802, 1804, and 1806 are plotted relative to time 1808 and cumulative gas 1810 (e.g., in millions of cubic feet (mcft)). Referring to FIG. 18B for the child well, the graph 1850 includes plots 1802, 1804, and 1806 for the three base cases. The plots 1802, 1804, and 1806 are plotted relative to time 1808 and cumulative gas 1810 (e.g., in millions of cubic feet (mcft)).

FIG. 19 is a plot 1900 showing an example of a gas saturation distribution of a parent well 1908 and a child well 1910, according to some implementations of the present disclosure. FIG. 19 shows a significant difference of both performance of gas cumulative. The child well has a production over 10× the production of the parent one, which is due to the high pressure drawdown in the child well. The gas saturation distribution is shown in FIG. 19 along both the wellbore and the vicinity of the wellbore. The pressure drawdown in the child well 1910 (“PROD2”) is greater than the parent well 1908. The impact of pressure drawdown is that gas production from the child well 1910 is greater than the parent well 1908. The gas saturation distribution is plotted relative to an i-direction 1902, a j-direction 1904, and a k-direction 1906, with intensities indicated by a pressure intensity key 1912 (e.g., in psi).

The different phenomenon of shear strain and shear stress can be shown to occur in the pair model. The child well has a higher shear strain and shear stress than the parent well (FIGS. 20 and 22 ). Meanwhile, the block 106,106,3 has the same performance for both of shear strain and shear stress (FIGS. 21 and 23 ).

FIG. 20 is a 3D view showing examples of shear strain in a parent well 2002 and a child well 2004, according to some implementations of the present disclosure. The shear strain is plotted relative to an i-direction 2006, a j-direction 2008, and a k-direction 2010, with intensities indicated by a shear strain key 2012.

FIG. 21 is a graph 2100 showing example shear strains in block 106,106,3 from the parent and child model, according to some implementations of the present disclosure. The shear strains, in the IJ direction 2102, in the IK direction 2104, and in the JK direction 2106 are plotted relative to time 2108 and shear strain 2110.

FIG. 22 is a 3D view showing examples of shear stress in a parent well 2202 and a child well 2204, according to some implementations of the present disclosure. The shear stress is plotted relative to an i-direction 2206, a j-direction 2208, and a k-direction 2210, with intensities indicated by a shear strain key 2212.

FIG. 23 is a graph 2300 showing example shear stress in block 106,106,3 from the parent and child model, according to some implementations of the present disclosure. The shear stresses, in the IJ direction 2302, in the IK direction 2304, and in the JK direction 2306 are plotted relative to time 2308 and shear strain 2310.

The parent and child wells are set up with the same method, but the results of the simulation show that the well head pressure (WHP) alteration of the child well has a dynamic rhythm compare to the parent well (FIG. 23 ).

FIG. 24 is a graph 2400 showing example well head pressures 2202 and 2204 of the parent well and the child well, according to some implementations of the present disclosure. The well head pressures 2202 and 2204 are plotted relative to time 2406 and WHP 2408.

Machine Learning Model Application

Different types of machine learning techniques can be applied to the dataset. For example, ML techniques can include: Ada Boost Regressor, Bagging Regressor, Bayesian Ridge, Decision Tree Regressor, Dummy Regressor, Elastic Net, Elastic Net cross-validation (CV), Extra Tree Regressor, Extra Trees Regressor, Gamma Regressor, Gaussian Process Regressor, Generalized Linear Regressor, Gradient Boosting Regressor, Histogram (Hist) Gradient Boosting Regressor, Huber Regressor, KNeighbors (k-nearest neighbors) Regressor, Kernel Ridge, Lars, Lars CV, Lasso, Lasso CV, Lasso Lars, Lasso Lars CV, Lasso Lars IC, Linear Regression, Linear Support Vector Regression (SVR), Multi-layer Perceptron (MLP) Regressor, NuSVR, Orthogonal Matching Pursuit, Orthogonal Matching Pursuit CV, Passive Aggressive Regressor, Poisson Regressor, RANdom SAmple Consensus (RANSAC) Regressor, Random Forest Regressor, Ridge, Ridge CV, SGD Regressor, SVR, Transformed Target Regressor, Tweedie Regressor, Extreme Gradient Boosting (XGB) Regressor, and Light Gradient Boosted Machine (LGBM) Regressor.

FIGS. 25-30 show example results of using models and predictions, according to some implementations of the present disclosure. For example, the results can be achieved using ML models applied to the data described previously, where cumulative production, pressure change, strain changes are modeled and predicted.

FIG. 25 includes a graph 2500 showing examples of machine learning model errors for different methods for cumulative condensate production at 12 months, according to some implementations of the present disclosure. Errors for different types of machine learning techniques 2502 are plotted in the graph 2500 relative to an error 2504 normalized to one and an error percentage 2506.

FIG. 26 includes a graph 2600 showing examples of predictions made using machine learning models for cumulative condensate production at 12 months, according to some implementations of the present disclosure. Predictions of cumulative (cum) condensate production 2606 are made for different cases 2604 for different types of ML techniques 2602.

FIG. 27 includes a graph 2700 showing examples of machine learning model errors for different methods for pressure change estimation at 500-ft away from a wellbore at 12 months, according to some implementations of the present disclosure. Adjusted R-squared errors for different types of machine learning techniques 2702 are plotted in the graph 2700 relative to an error 2704 normalized to one and an error percentage 2706.

FIG. 28 includes a graph 2800 showing examples of predictions made using machine learning models for pressure change estimation at 500-ft away from wellbore at 12 months, according to some implementations of the present disclosure. Pressure change predictions 2806 for different types of machine learning techniques 2802 are plotted in the graph 2800 relative to cases 2804.

FIG. 29 includes a graph 2900 showing examples of machine learning model errors for different methods for strain change in z-direction Estimation at 500-ft away from wellbore at 12 months, according to some implementations of the present disclosure. Adjusted R-squared errors for different types of machine learning techniques 2902 are plotted in the graph 2900 relative to an error 2904 normalized to one and an error percentage 2906.

FIG. 30 includes a graph 3000 showing examples of prediction of machine learning models methods for strain change in a z-direction, according to some implementations of the present disclosure. Estimations are at 500 feet away from wellbore at 12 months. Strain change predictions 3006 for different types of machine learning techniques 3002 are plotted in the graph 3000 relative to cases 3004.

FIG. 31 is a flowchart of an example of a method 3100 for determining optimum stress distributions for the placement of new wells, according to some implementations of the present disclosure. For clarity of presentation, the description that follows generally describes method 3100 in the context of the other figures in this description. However, it will be understood that method 3100 can be performed, for example, by any suitable system, environment, software, and hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various steps of method 3100 can be run in parallel, in combination, in loops, or in any order.

At 3102, stress change correlations over space and time are received for injection/production of fluids to/from a reservoir. For example, the data collected over time for a reservoir can include data described with reference to FIG. 14 . From 3102, method 3100 proceeds to 3104.

At 3104, a stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. For example, stress distributions can correspond to the shear stresses described with reference to FIGS. 14, 15, 22, and 23 . From 3104, method 3100 proceeds to 3106.

At 3106, fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. For example, fracture growth can correspond to the fracture growth described with reference to FIGS. 1, 4, 15, and 16 . From 3106, method 3100 proceeds to 3108.

At 3108, fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical and flow characteristics. From 3108, method 3100 proceeds to 3110.

At 3110, changes in the stress distribution in the reservoir are determined through injection/production of fluids. For example, stress distributions can correspond to the shear stresses described with reference to FIGS. 14, 15, 22, and 23 . From 3110, method 3100 proceeds to 3112.

At 3112, an optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. From 3112, method 3100 proceeds to 3114.

At 3114, an optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells. After 3114, method 3100 can stop.

In some implementations, method 3100 further includes generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model (for example, as described with reference to FIG. 4 ).

In some implementations, method 3100 further includes generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress (for example, as described with reference to FIG. 10 ).

In some implementations, method 3100 further includes generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of the well within the IJ direction (for example, as described with reference to FIG. 13 ).

In some implementations, method 3100 further includes generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well, according to some implementations of the present disclosure (for example, as described with reference to FIG. 17 ).

In some implementations, method 3100 further includes generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well, according to some implementations of the present disclosure (for example, as described with reference to FIG. 20 ).

In some implementations, method 3100 further includes generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well, according to some implementations of the present disclosure (for example, as described with reference to FIG. 22 ).

In some implementations, in addition to (or in combination with) any previously-described features, techniques of the present disclosure can include the following. Customized user interfaces can present intermediate or final results of the above described processes to a user. The presented information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or “app”), or at a central processing facility. The presented information can include suggestions, such as suggested changes in parameters or processing inputs, that the user can select to implement improvements in a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the suggestions can include parameters that, when selected by the user, can cause a change or an improvement in drilling parameters (including speed and direction) or overall production of a gas or oil well. The suggestions, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction. In some implementations, the suggestions can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time can correspond, for example, to events that occur within a specified period of time, such as within one minute or within one second. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.

FIG. 32 is a block diagram of an example computer system 3200 used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure, according to some implementations of the present disclosure. The illustrated computer 3202 is intended to encompass any computing device such as a server, a desktop computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer 3202 can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer 3202 can include output devices that can convey information associated with the operation of the computer 3202. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI).

The computer 3202 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 3202 is communicably coupled with a network 3230. In some implementations, one or more components of the computer 3202 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.

At a top level, the computer 3202 is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer 3202 can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.

The computer 3202 can receive requests over network 3230 from a client application (for example, executing on another computer 3202). The computer 3202 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 3202 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.

Each of the components of the computer 3202 can communicate using a system bus 3203. In some implementations, any or all of the components of the computer 3202, including hardware or software components, can interface with each other or the interface 3204 (or a combination of both) over the system bus 3203. Interfaces can use an application programming interface (API) 3212, a service layer 3213, or a combination of the API 3212 and service layer 3213. The API 3212 can include specifications for routines, data structures, and object classes. The API 3212 can be either computer-language independent or dependent. The API 3212 can refer to a complete interface, a single function, or a set of APIs.

The service layer 3213 can provide software services to the computer 3202 and other components (whether illustrated or not) that are communicably coupled to the computer 3202. The functionality of the computer 3202 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 3213, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer 3202, in alternative implementations, the API 3212 or the service layer 3213 can be stand-alone components in relation to other components of the computer 3202 and other components communicably coupled to the computer 3202. Moreover, any or all parts of the API 3212 or the service layer 3213 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.

The computer 3202 includes an interface 3204. Although illustrated as a single interface 3204 in FIG. 32 , two or more interfaces 3204 can be used according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. The interface 3204 can be used by the computer 3202 for communicating with other systems that are connected to the network 3230 (whether illustrated or not) in a distributed environment. Generally, the interface 3204 can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network 3230. More specifically, the interface 3204 can include software supporting one or more communication protocols associated with communications. As such, the network 3230 or the interface’s hardware can be operable to communicate physical signals within and outside of the illustrated computer 3202.

The computer 3202 includes a processor 3205. Although illustrated as a single processor 3205 in FIG. 32 , two or more processors 3205 can be used according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. Generally, the processor 3205 can execute instructions and can manipulate data to perform the operations of the computer 3202, including operations using algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure.

The computer 3202 also includes a database 3206 that can hold data for the computer 3202 and other components connected to the network 3230 (whether illustrated or not). For example, database 3206 can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database 3206 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. Although illustrated as a single database 3206 in FIG. 32 , two or more databases (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. While database 3206 is illustrated as an internal component of the computer 3202, in alternative implementations, database 3206 can be external to the computer 3202.

The computer 3202 also includes a memory 3207 that can hold data for the computer 3202 or a combination of components connected to the network 3230 (whether illustrated or not). Memory 3207 can store any data consistent with the present disclosure. In some implementations, memory 3207 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. Although illustrated as a single memory 3207 in FIG. 32 , two or more memories 3207 (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. While memory 3207 is illustrated as an internal component of the computer 3202, in alternative implementations, memory 3207 can be external to the computer 3202.

The application 3208 can be an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 3202 and the described functionality. For example, application 3208 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 3208, the application 3208 can be implemented as multiple applications 3208 on the computer 3202. In addition, although illustrated as internal to the computer 3202, in alternative implementations, the application 3208 can be external to the computer 3202.

The computer 3202 can also include a power supply 3214. The power supply 3214 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 3214 can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply 3214 can include a power plug to allow the computer 3202 to be plugged into a wall socket or a power source to, for example, power the computer 3202 or recharge a rechargeable battery.

There can be any number of computers 3202 associated with, or external to, a computer system containing computer 3202, with each computer 3202 communicating over network 3230. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 3202 and one user can use multiple computers 3202.

Described implementations of the subject matter can include one or more features, alone or in combination.

For example, in a first implementation, a computer-implemented method includes the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.

The foregoing and other described implementations can each, optionally, include one or more of the following features:

A first feature, combinable with any of the following features, the method further including generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.

A second feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.

A third feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.

A fourth feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.

A fifth feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.

A sixth feature, combinable with any of the previous or following features, the method further including generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well.

In a second implementation, a non-transitory, computer-readable medium stores one or more instructions executable by a computer system to perform operations including the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.

The foregoing and other described implementations can each, optionally, include one or more of the following features:

A first feature, combinable with any of the following features, the operations further including generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.

A second feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.

A third feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.

A fourth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.

A fifth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.

A sixth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well.

In a third implementation, a computer-implemented system includes one or more processors and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors. The programming instructions instruct the one or more processors to perform operations including the following. Stress change correlations are received over space and time for injection/production of fluids to/from a reservoir. A stress distribution of the reservoir is determined using reservoir geomechanical modeling tools and using the stress change correlations. Fracture growth/propagation behavior for the reservoir is determined using the stress distribution of the reservoir and using fracture modeling software and geomechanical properties for optimizing treatment. Fracture design and orientation needed for optimum recovery of hydrocarbons are determined by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical, and flow characteristics. Changes in the stress distribution in the reservoir are determined through injection/production of fluids. An optimized injection/production and placement of wells are determined using the changes in the stress distribution and the fracture design and orientation, including using machine learning to adjust injection and production of fluids to/from the reservoir. An optimum stress distribution for placement of new wells is determined using the optimized injection/production and placement of wells.

The foregoing and other described implementations can each, optionally, include one or more of the following features:

A first feature, combinable with any of the following features, the operations further including generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.

A second feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.

A third feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.

A fourth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.

A fifth feature, combinable with any of the previous or following features, the operations further including generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.

Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, such as LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub-programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory.

Graphics processing units (GPUs) can also be used in combination with CPUs. The GPUs can provide specialized processing that occurs in parallel to processing performed by CPUs. The specialized processing can include artificial intelligence (AI) applications and processing, for example. GPUs can be used in GPU clusters or in multi-GPU computing.

A computer can include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto-optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.

Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLU-RAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated into, special purpose logic circuitry.

Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that the user uses. For example, the computer can send web pages to a web browser on a user’s client device in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch-screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.

The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from aclaimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium. 

What is claimed is:
 1. A computer-implemented method, comprising: receiving stress change correlations over space and time for injection/production of fluids to/from a reservoir; determining, using reservoir geomechanical modeling tools and using the stress change correlations, a stress distribution of the reservoir; determining, using fracture modeling software and geomechanical properties for optimizing treatment, fracture growth/propagation behavior for the reservoir using the stress distribution of the reservoir; determining fracture design and orientation needed for optimum recovery of hydrocarbons by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical and flow characteristics; determining changes in the stress distribution in the reservoir through injection/production of fluids; determining, using the changes in the stress distribution and the fracture design and orientation, an optimized injection/production and placement of wells, including using machine learning adjust injection and production of fluids to/from the reservoir; and determining, using the optimized injection/production and placement of wells, an optimum stress distribution for placement of new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons.
 2. The computer-implemented method of claim 1, further comprising generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.
 3. The computer-implemented method of claim 1, further comprising generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.
 4. The computer-implemented method of claim 1, further comprising generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.
 5. The computer-implemented method of claim 1, further comprising generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.
 6. The computer-implemented method of claim 1, further comprising generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.
 7. The computer-implemented method of claim 1, further comprising generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well.
 8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: receiving stress change correlations over space and time for injection/production of fluids to/from a reservoir; determining, using reservoir geomechanical modeling tools and using the stress change correlations, a stress distribution of the reservoir; determining, using fracture modeling software and geomechanical properties for optimizing treatment, fracture growth/propagation behavior for the reservoir using the stress distribution of the reservoir; determining fracture design and orientation needed for optimum recovery of hydrocarbons by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical and flow characteristics; determining changes in the stress distribution in the reservoir through injection/production of fluids; determining, using the changes in the stress distribution and the fracture design and orientation, an optimized injection/production and placement of wells, including using machine learning adjust injection and production of fluids to/from the reservoir; and determining, using the optimized injection/production and placement of wells, an optimum stress distribution for placement of new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons.
 9. The non-transitory, computer-readable medium of claim 8, the operations further comprising generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.
 10. The non-transitory, computer-readable medium of claim 8, the operations further comprising generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.
 11. The non-transitory, computer-readable medium of claim 8, the operations further comprising generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.
 12. The non-transitory, computer-readable medium of claim 8, the operations further comprising generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.
 13. The non-transitory, computer-readable medium of claim 8, the operations further comprising generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well.
 14. The non-transitory, computer-readable medium of claim 8, the operations further comprising generating, for display in a user interface, a 3D plot of shear stress in a parent well and a child well.
 15. A computer-implemented system, comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising: receiving stress change correlations over space and time for injection/production of fluids to/from a reservoir; determining, using reservoir geomechanical modeling tools and using the stress change correlations, a stress distribution of the reservoir; determining, using fracture modeling software and geomechanical properties for optimizing treatment, fracture growth/propagation behavior for the reservoir using the stress distribution of the reservoir; determining fracture design and orientation needed for optimum recovery of hydrocarbons by analyzing relationships between fluid injection/withdrawal and geomechanical changes and the stress distribution, reservoir geomechanical and flow characteristics; determining changes in the stress distribution in the reservoir through injection/production of fluids; determining, using the changes in the stress distribution and the fracture design and orientation, an optimized injection/production and placement of wells, including using machine learning adjust injection and production of fluids to/from the reservoir; and determining, using the optimized injection/production and placement of wells, an optimum stress distribution for placement of new wells and fractures in terms of orientation and size to maximize recovery of hydrocarbons.
 16. The computer-implemented system of claim 15, the operations further comprising generating, for display in a user interface, a plot showing a single well pressure distribution for a single well model.
 17. The computer-implemented system of claim 15, the operations further comprising generating, for display in a user interface, a diagram showing a grid investigated for shear strain and shear stress.
 18. The computer-implemented system of claim 15, the operations further comprising generating, for display in a user interface, a three-dimensional (3D) plot showing different phenomena of shear strain between a toe and a heel of a well within an IJ direction.
 19. The computer-implemented system of claim 15, the operations further comprising generating, for display in a user interface, a plot showing a gas saturation distribution of a parent well and a child well.
 20. The computer-implemented system of claim 15, the operations further comprising generating, for display in a user interface, a 3D plot of shear strain in a parent well and a child well. 